Pooling Layers

Pooling Layers are listed in nnom_layers.h

1D/2D operations are both working with (H, W, C) format, known as "channel last". When working with 1D operations, the H for all the shapes must be 1 constantly.

MaxPool()

nnom_layer_t* MaxPool(nnom_3d_shape_t k, nnom_3d_shape_t s, nnom_padding_t pad);

This funtion is for 1D or 2D, mutiple channels max pooling.

Arguments

  • k (kernel): the kernel shape, which is returned by kernel()
  • s (stride): the stride shape, which is returned by stride()
  • pad (padding): the padding method PADDING_SAME or PADDING_VALID

Return

  • The layer instance

AvgPool()

nnom_layer_t* AvgPool(nnom_3d_shape_t k, nnom_3d_shape_t s, nnom_padding_t pad);

This funtion is for 1D or 2D, mutiple channels average pooling.

Arguments

  • k (kernel): the kernel shape, which is returned by kernel()
  • s (stride): the stride shape, which is returned by stride()
  • pad (padding): the padding method PADDING_SAME or PADDING_VALID

Return

  • The layer instance

Notes

Average pooling is not recommended to use with fixed-point model (such as here). Small values will be vanished when the sum is devided. (CMSIS-NN currently does not support changing the output shifting in average pooling from input to output.)

However, if the average pooling is the second last layer right before softmax layer, you can still use average pooling for training and then replaced by sumpooling in MCU. the only different is sumpooling (in NNoM) will not divide the sum directly but looking for a best shift (dynamic shifting) to cover the largest number.

SumPool()

nnom_layer_t* SumPool(nnom_3d_shape_t k, nnom_3d_shape_t s, nnom_padding_t pad);

This funtion is for 1D or 2D, mutiple channels Sum pooling. This is a better alternative to average pooling WHEN deploy the trained model into NNoM. The output shift for the sumpool in NNoM is dynamic, means that this pooling can only place before softmax layer.

Arguments

  • k (kernel): the kernel shape, which is returned by kernel()
  • s (stride): the stride shape, which is returned by stride()
  • pad (padding): the padding method PADDING_SAME or PADDING_VALID

Return

  • The layer instance

GlobalMaxPool()

nnom_layer_t *GlobalMaxPool(void);

Global Max Pooling

Return

  • The layer instance

GlobalAvgPool()

nnom_layer_t *GlobalAvgPool(void);

Global Average Pooling.

Due to the same reason as discussed in Average Pooling, it is recommended to replace this layer by GlobalSumPool() when the layer it she second last layer, and before the softmax layer.

If you used generate_model() to deploy your keras model to NNoM, this layer will be automaticly replaced by GlobalSumPool() when above conditions has met.

Return

  • The layer instance

GlobalSumPool()

nnom_layer_t *GlobalSumPool(void);

Global sum pooling.

Return

  • The layer instance